
"""simple docstring"""


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""
       if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
              # expert layer
              UpperCamelCase       			=		flax_key_tuple[:-1] + ("weight",)
              UpperCamelCase       			=		torch.permute(UpperCAmelCase_		,   (0, 2, 1)					)
       elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCAmelCase_					):
              # linear layer
              UpperCamelCase       			=		flax_key_tuple[:-1] + ("weight",)
              UpperCamelCase       			=		flax_tensor.T
       elif flax_key_tuple[-1] in ["scale", "embedding"]:
              UpperCamelCase       			=		flax_key_tuple[:-1] + ("weight",)

       return flax_key_tuple, flax_tensor



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				str:







       """simple docstring"""
       if "metadata" in layer:
              UpperCamelCase       			=		layer.split("metadata"					)
              UpperCamelCase       			=		"".join(split_layer[0]					)[:-1]
              UpperCamelCase       			=		[tuple(("metadata" + split_layer[1]).split("/"					)					)]
       elif "kvstore" in layer:
              UpperCamelCase       			=		layer.split("kvstore"					)
              UpperCamelCase       			=		"".join(split_layer[0]					)[:-1]
              UpperCamelCase       			=		[tuple(("kvstore" + split_layer[1]).split("/"					)					)]

       else:
              UpperCamelCase       			=		layer.split("/"					)
              UpperCamelCase       			=		"/".join(split_layer[:-1]					)
              UpperCamelCase       			=		(split_layer[-1],)

       if "kvstore/path" in layer:
              UpperCamelCase       			=		F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
       elif "kvstore/driver" in layer:
              UpperCamelCase       			=		"file"
       else:
              UpperCamelCase       			=		checkpoint_info[layer]

       return curr_real_layer_name, split_layer, content



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				str:







       """simple docstring"""
       UpperCamelCase       			=		rename_keys(UpperCAmelCase_					)
       UpperCamelCase       			=		{}
       for k, v in current_block.items():
              UpperCamelCase       			=		v
       UpperCamelCase       			=		new_current_block
       torch.save(UpperCAmelCase_		,   UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_ = WEIGHTS_NAME					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		convert_file_size_to_int(UpperCAmelCase_					)
       UpperCamelCase       			=		[]
       UpperCamelCase       			=		{}
       UpperCamelCase       			=		0
       UpperCamelCase       			=		0

       os.makedirs(UpperCAmelCase_		,   exist_ok=UpperCAmelCase_					)
       with gfile.GFile(switch_checkpoint_path + "/checkpoint"		,   "rb"					) as fp:
              UpperCamelCase       			=		serialization.msgpack_restore(fp.read()					)["optimizer"]["target"]
              UpperCamelCase       			=		flatten_dict(UpperCAmelCase_		,   sep="/"					)

       UpperCamelCase       			=		{}
       for layer in checkpoint_info.keys():
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		get_key_and_tensorstore_dict(
                  UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
              if curr_real_layer_name in all_layers:
                     UpperCamelCase       			=		content
              else:
                     UpperCamelCase       			=		{split_layer[-1]: content}

       for key in all_layers.keys():
              # open tensorstore file
              UpperCamelCase       			=		ts.open(unflatten_dict(all_layers[key]					)					).result().read().result()
              UpperCamelCase       			=		torch.tensor(UpperCAmelCase_					)
              UpperCamelCase       			=		raw_weights.numel() * dtype_byte_size(raw_weights.dtype					)

              # use the renaming pattern from the small conversion scripts
              UpperCamelCase			,							UpperCamelCase       			=		rename_base_flax_keys(tuple(key.split("/"					)					)		,   UpperCAmelCase_					)
              UpperCamelCase       			=		"/".join(UpperCAmelCase_					)

              # If this weight is going to tip up over the maximal size, we split.
              if current_block_size + weight_size > max_shard_size:
                     UpperCamelCase       			=		os.path.join(
                         UpperCAmelCase_		,   weights_name.replace(".bin"		,   F"-{len(UpperCAmelCase_					)+1:05d}-of-???.bin"					)					)
                     rename_and_save_block(UpperCAmelCase_		,   UpperCAmelCase_					)
                     sharded_state_dicts.append(current_block.keys()					)
                     del current_block
                     UpperCamelCase       			=		{}
                     UpperCamelCase       			=		0

              UpperCamelCase       			=		raw_weights.to(getattr(UpperCAmelCase_		,   UpperCAmelCase_					)					)
              current_block_size += weight_size
              total_size += weight_size

       # Add the last block
       UpperCamelCase       			=		os.path.join(UpperCAmelCase_		,   weights_name.replace(".bin"		,   F"-{len(UpperCAmelCase_					)+1:05d}-of-???.bin"					)					)
       rename_and_save_block(UpperCAmelCase_		,   UpperCAmelCase_					)
       sharded_state_dicts.append(current_block.keys()					)

       # If we only have one shard, we return it
       if len(UpperCAmelCase_					) == 1:
              return {weights_name: sharded_state_dicts[0]}, None

       # Otherwise, let's build the index
       UpperCamelCase       			=		{}
       UpperCamelCase       			=		{}
       for idx, shard in enumerate(UpperCAmelCase_					):
              UpperCamelCase       			=		weights_name.replace(
                  ".bin"		,   F"-{idx+1:05d}-of-{len(UpperCAmelCase_					):05d}.bin"					)  # len(sharded_state_dicts):05d}
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_		,   weights_name.replace(".bin"		,   F"-{idx+1:05d}-of-???.bin"					)					)
              os.rename(UpperCAmelCase_		,   os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					)					)
              UpperCamelCase       			=		shard
              for key in shard:
                     UpperCamelCase       			=		shard_file

    # Add the metadata
       UpperCamelCase       			=		{"total_size": total_size}
       UpperCamelCase       			=		{"metadata": metadata, "weight_map": weight_map}

       with open(os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					)		,   "w"		,   encoding="utf-8"					) as f:
              UpperCamelCase       			=		json.dumps(UpperCAmelCase_		,   indent=2		,   sort_keys=UpperCAmelCase_					) + "\n"
              f.write(UpperCAmelCase_					)

       return metadata, index


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--switch_t5x_checkpoint_path""",
          default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
          type=str,
          required=False,
          help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
      )
      parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
      parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
          type=str,
          required=False,
          help="""Path to the output pytorch model.""",
      )
      SCREAMING_SNAKE_CASE			  =  parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )



def 						lowerCamelCase__      (				)->				Union[str, Any]:







       """simple docstring"""
       from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

       UpperCamelCase       			=		SwitchTransformersConfig.from_pretrained("google/switch-base-8"					)
       config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"					)
       UpperCamelCase       			=		SwitchTransformersForConditionalGeneration.from_pretrained(
           "/home/arthur_huggingface_co/transformers/switch_converted"		,   device_map="auto"					)

       UpperCamelCase       			=		TaTokenizer.from_pretrained("t5-small"					)
       UpperCamelCase       			=		"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

       UpperCamelCase       			=		tokenizer(UpperCAmelCase_		,   return_tensors="pt"					).input_ids
       UpperCamelCase       			=		model.generate(UpperCAmelCase_		,   decoder_start_token_id=0					)
       print(tokenizer.decode(out[0]					)					)







"""simple docstring"""


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       return max(metric_fn(UpperCAmelCase_		,   UpperCAmelCase_					) for gt in ground_truths					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[]

       if args.gold_data_mode == "qa":
              UpperCamelCase       			=		pd.read_csv(UpperCAmelCase_		,   sep="\t"		,   header=UpperCAmelCase_					)
              for answer_list in data[1]:
                     UpperCamelCase       			=		ast.literal_eval(UpperCAmelCase_					)
                     answers.append(UpperCAmelCase_					)
       else:
              UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
              UpperCamelCase       			=		[[reference] for reference in references]

       UpperCamelCase       			=		UpperCamelCase       			=		UpperCamelCase       			=		0
       for prediction, ground_truths in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              total += 1
              em += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
              fa += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		100.0 * em / total
       UpperCamelCase       			=		100.0 * fa / total

       logger.info(F"F1: {fa:.2f}"					)
       logger.info(F"EM: {em:.2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		args.k
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]

       UpperCamelCase       			=		UpperCamelCase       			=		0
       for hypo, reference in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		set(hypo.split("\t"					)[:k]					)
              UpperCamelCase       			=		set(reference.split("\t"					)					)
              total += 1
              em += len(hypo_provenance & ref_provenance					) / k

       UpperCamelCase       			=		100.0 * em / total
       logger.info(F"Precision@{k}: {em: .2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       def strip_title(UpperCAmelCase_					):
              if title.startswith("\""					):
                     UpperCamelCase       			=		title[1:]
              if title.endswith("\""					):
                     UpperCamelCase       			=		title[:-1]
              return title

       UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_		,   )["input_ids"].to(args.device					)

       UpperCamelCase       			=		rag_model.rag.question_encoder(UpperCAmelCase_					)
       UpperCamelCase       			=		question_enc_outputs[0]

       UpperCamelCase       			=		rag_model.retriever(
           UpperCAmelCase_		,   question_enc_pool_output.cpu().detach().to(torch.floataa					).numpy()		,   prefix=rag_model.rag.generator.config.prefix		,   n_docs=rag_model.config.n_docs		,   return_tensors="pt"		,   )
       UpperCamelCase       			=		rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
       UpperCamelCase       			=		[]
       for docs in all_docs:
              UpperCamelCase       			=		[strip_title(UpperCAmelCase_					) for title in docs["title"]]
              provenance_strings.append("\t".join(UpperCAmelCase_					)					)
       return provenance_strings



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       with torch.no_grad():
              UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_					)

              UpperCamelCase       			=		inputs_dict.input_ids.to(args.device					)
              UpperCamelCase       			=		inputs_dict.attention_mask.to(args.device					)
              UpperCamelCase       			=		rag_model.generate(  # rag_model overwrites generate
                  UpperCAmelCase_		,   attention_mask=UpperCAmelCase_		,   num_beams=args.num_beams		,   min_length=args.min_length		,   max_length=args.max_length		,   early_stopping=UpperCAmelCase_		,   num_return_sequences=1		,   bad_words_ids=[[0, 0]]		,   )
              UpperCamelCase       			=		rag_model.retriever.generator_tokenizer.batch_decode(UpperCAmelCase_		,   skip_special_tokens=UpperCAmelCase_					)

              if args.print_predictions:
                     for q, a in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
                            logger.info("Q: {} - A: {}".format(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              return answers



def 						lowerCamelCase__      (				)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser()
       parser.add_argument(
           "--model_type"		,   choices=["rag_sequence", "rag_token", "bart"]		,   type=UpperCAmelCase_		,   help=(
               "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
               " model_name_or_path"
           )		,   )
       parser.add_argument(
           "--index_name"		,   default=UpperCAmelCase_		,   choices=["exact", "compressed", "legacy"]		,   type=UpperCAmelCase_		,   help="RAG model retriever type"		,   )
       parser.add_argument(
           "--index_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   help="Path to the retrieval index"		,   )
       parser.add_argument("--n_docs"		,   default=5		,   type=UpperCAmelCase_		,   help="Number of retrieved docs"					)
       parser.add_argument(
           "--model_name_or_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to pretrained checkpoints or model identifier from huggingface.co/models"		,   )
       parser.add_argument(
           "--eval_mode"		,   choices=["e2e", "retrieval"]		,   default="e2e"		,   type=UpperCAmelCase_		,   help=(
               "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
               " precision@k."
           )		,   )
       parser.add_argument("--k"		,   default=1		,   type=UpperCAmelCase_		,   help="k for the precision@k calculation"					)
       parser.add_argument(
           "--evaluation_set"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a file containing evaluation samples"		,   )
       parser.add_argument(
           "--gold_data_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a tab-separated file with gold samples"		,   )
       parser.add_argument(
           "--gold_data_mode"		,   default="qa"		,   type=UpperCAmelCase_		,   choices=["qa", "ans"]		,   help=(
               "Format of the gold data file"
               "qa - a single line in the following format: question [tab] answer_list"
               "ans - a single line of the gold file contains the expected answer string"
           )		,   )
       parser.add_argument(
           "--predictions_path"		,   type=UpperCAmelCase_		,   default="predictions.txt"		,   help="Name of the predictions file, to be stored in the checkpoints directory"		,   )
       parser.add_argument(
           "--eval_all_checkpoints"		,   action="store_true"		,   help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"		,   )
       parser.add_argument(
           "--eval_batch_size"		,   default=8		,   type=UpperCAmelCase_		,   help="Batch size per GPU/CPU for evaluation."		,   )
       parser.add_argument(
           "--recalculate"		,   help="Recalculate predictions even if the prediction file exists"		,   action="store_true"		,   )
       parser.add_argument(
           "--num_beams"		,   default=4		,   type=UpperCAmelCase_		,   help="Number of beams to be used when generating answers"		,   )
       parser.add_argument("--min_length"		,   default=1		,   type=UpperCAmelCase_		,   help="Min length of the generated answers"					)
       parser.add_argument("--max_length"		,   default=50		,   type=UpperCAmelCase_		,   help="Max length of the generated answers"					)

       parser.add_argument(
           "--print_predictions"		,   action="store_true"		,   help="If True, prints predictions while evaluating."		,   )
       parser.add_argument(
           "--print_docs"		,   action="store_true"		,   help="If True, prints docs retried while generating."		,   )
       UpperCamelCase       			=		parser.parse_args()
       UpperCamelCase       			=		torch.device("cuda" if torch.cuda.is_available() else "cpu"					)
       return args



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       if args.model_type is None:
              UpperCamelCase       			=		infer_model_type(args.model_name_or_path					)
              assert args.model_type is not None
       if args.model_type.startswith("rag"					):
              UpperCamelCase       			=		RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
              UpperCamelCase       			=		args.n_docs
              if args.index_name is not None:
                     UpperCamelCase       			=		args.index_name
              if args.index_path is not None:
                     UpperCamelCase       			=		args.index_path
       else:
              UpperCamelCase       			=		BartForConditionalGeneration

       UpperCamelCase       			=		(
           [f.path for f in os.scandir(args.model_name_or_path					) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info("Evaluate the following checkpoints: %s"		,   UpperCAmelCase_					)

       UpperCamelCase       			=		get_scores if args.eval_mode == "e2e" else get_precision_at_k
       UpperCamelCase       			=		evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path					) and (not args.recalculate):
                     logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path					)					)
                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)
                     continue

              logger.info("***** Running evaluation for {} *****".format(UpperCAmelCase_					)					)
              logger.info("  Batch size = %d"		,   args.eval_batch_size					)
              logger.info("  Predictions will be stored under {}".format(args.predictions_path					)					)

              if args.model_type.startswith("rag"					):
                     UpperCamelCase       			=		RagRetriever.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   retriever=UpperCAmelCase_		,   **UpperCAmelCase_					)
                     model.retriever.init_retrieval()
              else:
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
              model.to(args.device					)

              with open(args.evaluation_set		,   "r"					) as eval_file, open(args.predictions_path		,   "w"					) as preds_file:
                     UpperCamelCase       			=		[]
                     for line in tqdm(UpperCAmelCase_					):
                            questions.append(line.strip()					)
                            if len(UpperCAmelCase_					) == args.eval_batch_size:
                                   UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                                   preds_file.write("\n".join(UpperCAmelCase_					) + "\n"					)
                                   preds_file.flush()
                                   UpperCamelCase       			=		[]
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                            preds_file.write("\n".join(UpperCAmelCase_					)					)
                            preds_file.flush()

                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  get_args()
      main(args)





